ArticleScientific reports2021
A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed, 60 citations in OpenAlex.
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- Differential diagnosis of benign lesions and lung adenocarcinoma presenting as lung-RADS 2022 category 4B solid nodules based on multiscale CT radiomics.BMC cancer · 2026Article
- Decoding the Rotation Effect: A Retrospective Analysis of Lesion Orientation and Its Impact on Wavelet-Based Radiomics Feature Extraction and Lung Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.Journal of the Egyptian National Cancer Institute · 2025Article
- Multiparametric MRI-based radiomics nomogram for noninvasive stratification of HER2 expression status in breast cancer.Quantitative imaging in medicine and surgery · 2025Article
- A prognostic model integrating radiomics and deep learning based on CT for survival prediction in laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules.European radiology · 2025Article
- Differentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach.The oncologist · 2025Article
- A novel MRI-based deep learning-radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection.Frontiers in medicine · 2025Article
- Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease.Frontiers in neurology · 2025Article
- Multi-type classification of lung nodules based on CT radiomics and ensemble learning for diversity weighting.Quantitative imaging in medicine and surgery · 2024Article
- Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease.European radiology · 2024Article
- Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.BMC medical imaging · 2024Article
- Radiomic features based on pyradiomics predict CD276 expression associated with breast cancer prognosis.Heliyon · 2024Article
- Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.Translational lung cancer research · 2024Article
- Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.European radiology · 2024Article
- Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study.European radiology · 2024Article
- Radiomics approach to distinguish between benign and malignant soft tissue tumors on magnetic resonance imaging.European journal of radiology open · 2024Article
- The diagnostic value of multimodal imaging based on MR combined with ultrasound in benign and malignant breast diseases.Clinical and experimental medicine · 2024Article
- A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.Diagnostics (Basel, Switzerland) · 2024Article
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8 authors at 4 institutions in 1 country.
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Abstract
This study was to develop a radiomics nomogram mainly using wavelet features for identifying malignant and benign early-stage lung nodules for high-risk screening. A total of 116 patients with early-stage solitary pulmonary nodules (SPNs) (≤ 3 cm) were divided into a training set (N = 70) and a validation set (N = 46). Radiomics features were extracted from plain LDCT images of each patient. A radiomics signature was then constructed with the LASSO with the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. This radiomics signature, consisting of one original and nine wavelet features, achieved favorable predictive efficacy than Mayo Clinic Model. The radiomics nomogram with radiomics signature and age also showed good calibration and discrimination in the training set (AUC 0.9406; 95% CI 0.8831-0.9982) and the validation set (AUC 0.8454; 95% CI 0.7196-0.9712). The decision curve indicated the clinical usefulness of our nomogram. The presented radiomics nomogram shows favorable predictive accuracy for identifying malignant and benign lung nodules in early-stage patients and is much better than the Mayo Clinic Model.
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